Tomography Magnetic Resonance Imaging Research Articles

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

Management of sports injury treatment and radiological data analysis based on enhanced MRI image retrieval using autoencoder-based deep learning

BackgroundThe retrieval of magnetic resonance imaging (MRI) data holds paramount importance in clinical settings and sports medicine due to the limitations of conventional methods, such as slow speed, low accuracy, and limited learning capabilities. Enhancing this retrieval process is critical for advancing sports injury diagnostics and treatment outcomes. Overcoming these challenges is vital for improving healthcare practices and sports medicine methodologies. MethodThis study investigates the utilization of autoencoders in deep learning to efficiently retrieve MRI data from databases for sports injury diagnosis and treatment, with a focus on the model's ability to be trained with a small amount of labeled data. This research aims to enhance the MRI data retrieval process by leveraging autoencoders, showcasing the potential of deep learning technologies in sports injury diagnostics without the necessity of extensive labeled datasets for training. ResultsFindings have showcased the remarkable benefits of this approach for MRI data retrieval tasks, achieving an average accuracy of 99.09%. This signifies the exceptional performance of the technique within this specific domain, demonstrating its effectiveness and reliability in extracting MRI data. ConclusionsThis innovative methodology can enhance the management of archival data and diagnostic capabilities of medical images in sports injury contexts, offering an efficient and dependable solution for MRI data retrieval. It not only facilitates rapid clinical diagnosis and sports medicine research but also proposes a convenient approach for medical image file management.

Assessment of image quality and diagnostic accuracy for cervical spondylosis using T2w-STIR sequence with a deep learning-based reconstruction approach.

To investigate potential of enhancing image quality, maintaining interobserver consensus, and elevating disease diagnostic efficacy through the implementation of deep learning-based reconstruction (DLR) processing in 3.0T cervical spine fast magnetic resonance imaging (MRI) images, compared with conventional images. The 3.0T cervical spine MRI images of 71 volunteers were categorized into two groups: sagittal T2-weighted short T1 inversion recovery without DLR (Sag T2w-STIR) and with DLR (Sag T2w-STIR-DLR). The assessment covered artifacts, perceptual signal-to-noise ratio, clearness of tissue interfaces, fat suppression, overall image quality, and the delineation of spinal cord, vertebrae, discs, dopamine, and joints. Spanning canal stenosis, neural foraminal stenosis, herniated discs, annular fissures, hypertrophy of the ligamentum flavum or vertebral facet joints, and intervertebral disc degeneration were evaluated by three impartial readers. Sag T2w-STIR-DLR images exhibited markedly superior performance across quality indicators (median = 4 or 5) compared to Sag T2w-STIR sequences (median = 3 or 4) (p < 0.001). No statistically significant differences were observed between the two sequences in terms of diagnosis and grading (p > 0.05). The interobserver agreement for Sag T2w-STIR-DLR images (0.604-0.931) was higher than the other (0.545-0.853), Sag T2w-STIR-DLR (0.747-1.000) demonstrated increased concordance between reader 1 and reader 3 in comparison to Sag T2w-STIR (0.508-1.000). Acquisition time diminished from 364 to 197s through the DLR scheme. Our investigation establishes that 3.0T fast MRI images subjected to DLR processing present heightened image quality, bolstered diagnostic performance, and reduced scanning durations for cervical spine MRI compared with conventional sequences.

Comparative evaluation and correlation of variations in articular disc morphology as assessed by automated segmentation using deep learning on magnetic resonance imaging (MRI) images in Class II (vertical) TMD cases, Class II (horizontal) TMD cases and Class I non-TMD cases

Introduction: Temporomandibular disorder (TMD) encompasses several clinical manifestations, which are characterized by temporomandibular joint and masticatory muscle discomfort and dysfunction (TMJ). The best imaging technique for evaluating TMJ is magnetic resonance imaging (MRI), which makes it possible to see the anatomical and pathological characteristics of every joint component. In recent years, convolutional neural networks -based deep learning algorithms have been favoured because of their outstanding capability in recognizing objects in medical images. The objective of this study is to assess, compare and co-rrelate articular disc morphology by automated segmentation using deep learning on MRI images in skeletal Class II (vertical growth pattern) TMD cases as compared to skeletal Class II (horizontal growth pattern) TMD cases and Class I non-TMD cases Methods: Grading of skeletal Class II (vertical growth pattern) cases and skeletal Class II (horizontal growth pattern) cases based on severity of TMD will be carried out using diagnostic criteria for temporomandibular disorders. Bilateral sagittal as well as coronal MRI images will be obtained. A convolutional neural network (CNN) encoder-decoder named U-Net will be used to segment the articular disc on MRI. Understanding the nature of variations between Class I and both types of Class IIs will help orthodontists to better predict the potential risk for the development of TMDs and accordingly take precautions while doing treatment in such cases. Moreover, it can be used to automate TMD diagnosis and other smart applications. Conclusions: This study will aid in identifying articular disc morphology on MRI. The deep learning algorithms with effective data augmentation may perform better in MRI readings than human clinicians when using the same data, which will be advantageous for TMD diagnosis.

Automated magnetic resonance imaging-based grading of the lumbar intervertebral disc and facet joints.

Degeneration of both intervertebral discs (IVDs) and facet joints in the lumbar spine has been associated with low back pain, but whether and how IVD/joint degeneration contributes to pain remains an open question. Joint degeneration can be identified by pairing T1 and T2 magnetic resonance imaging (MRI) with analysis techniques such as Pfirrmann grades (IVD degeneration) and Fujiwara scores (facet degeneration). However, these grades are subjective, prompting the need to develop an automated technique to enhance inter-rater reliability. This study introduces an automated convolutional neural network (CNN) technique trained on clinical MRI images of IVD and facet joints obtained from public-access Lumbar Spine MRI Dataset. The primary goal of the automated system is to classify health of lumbar discs and facet joints according to Pfirrmann and Fujiwara grading systems and to enhance inter-rater reliability associated with these grading systems. Performance of the CNN on both the Pfirrmann and Fujiwara scales was measured by comparing the percent agreement, Pearson's correlation and Fleiss kappa value for results from the classifier to the grades assigned by an expert grader. The CNN demonstrates comparable performance to human graders for both Pfirrmann and Fujiwara grading systems, but with larger errors in Fujiwara grading. The CNN improves the reliability of the Pfirrmann system, aligning with previous findings for IVD assessment. The study highlights the potential of using deep learning in classifying the IVD and facet joint health, and due to the high variability in the Fujiwara scoring system, highlights the need for improved imaging and scoring techniques to evaluate facet joint health. All codes required to use the automatic grading routines described herein are available in the Data Repository for University of Minnesota (DRUM).

Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI.

Lumbar disc herniation is one of the most prevalent orthopedic issues in clinical practice. The lumbar spine is a crucial joint for movement and weight-bearing, so back pain can significantly impact the everyday lives of patients and is prone to recurring. The pathogenesis of lumbar disc herniation is complex and diverse, making it difficult to identify and assess after it has occurred. Magnetic resonance imaging (MRI) is the most effective method for detecting injuries, requiring continuous examination by medical experts to determine the extent of the injury. However, the continuous examination process is time-consuming and susceptible to errors. This study proposes an enhanced model, BE-YOLOv5, for hierarchical detection of lumbar disc herniation from MRI images. To tailor the training of the model to the job requirements, a specialized dataset was created. The data was cleaned and improved before the final calibration. A final training set of 2083 data points and a test set of 100 data points were obtained. The YOLOv5 model was enhanced by integrating the attention mechanism module, ECAnet, with a 3 × 3 convolutional kernel size, substituting its feature extraction network with a BiFPN, and implementing structural system pruning. The model achieved an 89.7% mean average precision (mAP) and 48.7 frames per second (FPS) on the test set. In comparison to Faster R-CNN, original YOLOv5, and the latest YOLOv8, this model performs better in terms of both accuracy and speed for the detection and grading of lumbar disc herniation from MRI, validating the effectiveness of multiple enhancement methods. The proposed model is expected to be used for diagnosing lumbar disc herniation from MRI images and to demonstrate efficient and high-precision performance.

MicroRNA and renal fibrosis in autosomal dominant polycystic kidney disease: a longitudinal study.

Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary kidney disorder that may progress to kidney failure, accounting for 5-10% of all patients with end-stage kidney disease (ESKD). Clinical data, as well as molecular genetics and advanced imaging techniques have provided surrogate prognostic biomarkers to predict rapid decline in kidney function, nonetheless enhanced tools for assessing prognosis for ADPKD are stillneeded. The aim of this study was to analyze specific microRNAs involved in the pathogenesis of ADPKD and in the development of renal fibrosis, evaluating their potential role as predictors of renal function loss. We evaluated kidney function by estimated glomerular filtration rate (eGFR) in 32 ADPKD patients in different stages of kidney disease at T0 and after a 24-month follow up (T1). Patients were divided into two groups: Rapid disease progression ([RP], n 15) and Non-rapid disease progression ([NRP], n 17), according to the Mayo Clinic classification criteria. At T0, ADPKD patients underwent plasma sampling for quantitative analysis of h-miR-17-5p, h-miR-21-5p and h-miR-199a-5p microRNA expression, using the quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) method and a 3T magnetic resonance imaging (MRI), using an advanced MRI imaging protocol, for the quantification of total kidney volume (TKV), total perfusion volume (TPV) and total fibrotic volume (TFV). The expression of h-miR17-5p was higher (p < 0.05) in ADPKD patients with rapid disease progression. h-miR-17-5p, h-miR-21-5p and h-mir-199-5p showed a positive and significant correlation with the eGFR slope (mL/min/1.73 m2/year) (p < 0.05) but not with the eGFR at both T0 and T1. Both total fibrotic volume (cm3) and height-adjusted total fibrotic volume (cm3/m) were positively and significantly correlated to h-miR 21-5p and h-miR 199-5p (p < 0.05), but not to total kidney volume (cm3) and height-adjusted total kidney volume (cm3/m). The microRNAs we studied were associated with fibrosis and renal damage, suggesting their possible role as biomarkers able to identify ADPKD patients at high risk of disease progression regardless of the degree of kidney function, and therefore suitable for medical therapy, and may help uncovering new molecular mechanisms underlying cystogenesis.

Design and Dynamic In Vivo Validation of a Multi-Channel Stretchable Liquid Metal Coil Array.

Recent developments in the field of radiofrequency (RF) coils for magnetic resonance imaging (MRI) offer flexible and patient-friendly solutions. Previously, we demonstrated a proof-of-concept single-element stretchable coil design based on liquid metal and a self-tuning smart geometry. In this work, we numerically analyze and experimentally study a multi-channel stretchable coil array and demonstrate its application in dynamic knee imaging. We also compare our flexible coil array to a commonly used commercial rigid coil array. Our numerical analysis shows that the proposed coil array maintains its resonance frequency (<1% variation) and sensitivity (<6%) at various stretching configurations from 0% to 30%. We experimentally demonstrate that the signal-to-noise ratio (SNR) of the acquired MRI images is improved by up to four times with the stretchable coil array due to its conformal and therefore tight-fitting nature. This stretchable array allows for dynamic knee imaging at different flexion angles, infeasible with traditional, rigid coil arrays. These findings are significant as they address the limitations of current rigid coil technology, offering a solution that enhances patient comfort and image quality, particularly in applications requiring dynamic imaging.

Locus coeruleus contrast and diffusivity metrics differentially relate to age and memory performance

Neurocognitive aging researchers are increasingly focused on the locus coeruleus, a neuromodulatory brainstem structure that degrades with age. With this rapid growth, the field will benefit from consensus regarding which magnetic resonance imaging (MRI) metrics of locus coeruleus structure are most sensitive to age and cognition. To address this need, the current study acquired magnetization transfer- and diffusion-weighted MRI images in younger and older adults who also completed a free recall memory task. Results revealed significantly larger differences between younger and older adults for maximum than average magnetization transfer-weighted contrast (MTC), axial than mean or radial single-tensor diffusivity (DTI), and free than restricted multi-compartment diffusion (NODDI) metrics in the locus coeruleus; with maximum MTC being the best predictor of age group. Age effects for all imaging modalities interacted with sex, with larger age group differences in males than females for MTC and NODDI metrics. Age group differences also varied across locus coeruleus subdivision for DTI and NODDI metrics, and across locus coeruleus hemispheres for MTC. Within older adults, however, there were no significant effects of age on MTC or DTI metrics, only an interaction between age and sex for free diffusion. Finally, independent of age and sex, higher restricted diffusion in the locus coeruleus was significantly related to better (lower) recall variability, but not mean recall. Whereas MTC has been widely used in the literature, our comparison between the average and maximum MTC metrics, inclusion of DTI and NODDI metrics, and breakdowns by locus coeruleus subdivision and hemisphere make important and novel contributions to our understanding of the aging of locus coeruleus structure.

Advantages of cone beam computed tomography for evaluation of subchondral insufficiency fractures of the knee compared to MRI

To determine the diagnostic yield of cone beam computed tomography (CBCT) compared with 3 T magnetic resonance imaging (MRI) for the evaluation of subchondral insufficiency fractures of the knee. Consecutive patients with subchondral insufficiency fractures of the knee examined by 3 T MRI and CBCT of the femoral condyles were reviewed. Two experienced raters graded the lesion severity on 3 T MRI and CBCT images: grade 1: no signs of a subchondral bone lesion; grade 2: subchondral trabecular fracture or cystic changes, but without infraction of the subchondral bone plate; grade 3: collapse of the subchondral bone plate. Ratings were repeated after six weeks to determine reliability. In addition, the bone lesion size was measured as elliptical area (mm2) and compared between CBCT and T1-weighted MRI sequences. Among 30 patients included (43.3% women; mean age: 60.9 ± 12.8 years; body mass index (BMI) 29.0 ± 12.8 kg/m2), the medial femoral condyle was affected in 21/30 patients (70%). The grading of subchondral lesions between MRI and CBCT did not match in 12 cases (40%). Based on MRI images, an underestimation (i.e., undergrading) compared with CBCT was observed in nine cases (30%), whereas overgrading occurred in three cases (10%). Compared to CBCT, routine T1-weighted 3 T sequences significantly overestimated osseus defect zones in sagittal (84.7 ± 68.9 mm2 vs. 35.9 ± 38.2 mm2, p < 0.01, Cohen’s d = 1.14) and coronal orientation (53.1 ± 24.0 mm2 vs. 22.0 ± 15.2 mm2, p < 0.01, Cohen’s d = 1.23). The reproducibility of the grading determined by intra- and inter-rater agreement was very high in MRI (intra-class correlation coefficient (ICC) 0.78 and 0.90, respectively) and CBCT (ICC 0.96 and 0.96, respectively). In patients with subchondral insufficiency fractures of the knee, the use of CBCT revealed discrepancies in lesion grading compared with MRI. These findings are clinically relevant, as precise determination of subchondral bone plate integrity may influence the decision about conservative or surgical treatment. CBCT represents our imaging modality of choice for grading the lesion and assessing subchondral bone plate integrity. MRI remains the gold standard modality to detect especially early stages.

Imaging Spectrum of COVID-19 Associated Cranio-Facial Mucormycosis

Abstract Background The post COVID craniofacial mucormycosis was found to emerge since the second wave of the COVID pandemics. It was called black fungus as it was associated with high mortality rate. Objective To describe combined computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of post COVID craniofacial mucormycosis for accurate diagnosis and determination of the extent of involvement. Patients and Methods The main source of data for this study was the archived CT and MRI imaging of the patients referred to the department of Radiology, Ain Shams University Hospitals. Results Our study population presented with craniofacial symptoms within 1-8 weeks from COVID-19 infection. The study revealed strong association between history of ICU admission, high doses of corticosteroids, oxygen administration and sever imaging MRI features of fungal sinusitis. Fungal invasion is not necessary to be through bony destruction. Conclusion The severest form of fungal sinusitis was found to be more commonly associated with ICU admission, usage of high doses of corticosteroids as well as patients who needed O2 therapy, the usage of contrast enhanced MRI should be combined with CT for assessment of disease extension as well as bony invasion.

Anterior cruciate ligament tear detection using gated recurrent unit and flexible fitness dependent optimizer

Anterior Cruciate Ligament (ACL) tear is a common knee injury that can be diagnosed using Magnetic Resonance Imaging (MRI). In this study, a novel approach has been proposed for improving the accuracy of ACL tear detection using an optimized Gated Recurrent Unit (GRU) model. The GRU model is a type of Recurrent Neural Network (RNN) that can capture the temporal features of MRI images. The optimization of the GRU model is based on a modified metaheuristic technique, called the Flexible Fitness Dependent Optimizer (FFDO). The FFDO is a flexible and problem-dependent algorithm that can find the best solutions to optimize problems by generating and evaluating a set of candidate solutions based on a fitness function. The proposed approach is applied to the MRNet dataset, and the results are compared with 17 state-of-the-art methods. The results show that the proposed approach can achieve a high accuracy of97.95%for ACL tear detection, which is higher than the existing deep learning and machine learning methods. The proposed approach can also achieve high values for other performance metrics, such as specificity, sensitivity, precision, MCC, F1-score, and AUC. The proposed approach can effectively extract the essential features of knee MRI images that are indicative of ACL tears with high diagnostic accuracy.

Brain magnetic resonance image (MRI) segmentation using multimodal optimization

AbstractOne of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters.

Optimal extreme learning machine for diagnosing brain tumor based on modified sailfish optimizer

This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then used to diagnose brain tumors that are integrated with the Modified Sailfish optimizer to enhance its performance. The Modified Sailfish optimizer is a metaheuristic algorithm known for efficiently navigating optimization landscapes and enhancing convergence speed. Experiments were conducted using the “Whole Brain Atlas (WBA)” database, which contains annotated MRI images. The results showed superior efficiency in accurately detecting brain tumors from MRI images, demonstrating the potential of the method in enhancing accuracy and efficiency. The proposed method utilizes hierarchical methodology, preprocessing techniques, and optimization of the Extreme Learning Machine with the Modified Sailfish optimizer to improve accuracy rates and decrease the time needed for brain tumor diagnosis. The proposed method outperformed other methods in terms of accuracy, recall, specificity, precision, and F1 score in medical imaging diagnosis. It achieved the highest accuracy at 93.95 %, with End/End and CNN attaining high values of 89.24 % and 93.17 %, respectively. The method also achieved a perfect score of 100 % in recall, 91.38 % in specificity, and 75.64 % in F1 score. However, it is crucial to consider factors like computational complexity, dataset characteristics, and generalizability before evaluating the effectiveness of the method in medical imaging diagnosis. This approach has the potential to make substantial contributions to medical imaging and aid healthcare professionals in making prompt and precise treatment decisions for brain tumors.

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

MRI-guided in-bore biopsy of the prostate – defining the optimal number of cores needed

BackgroundNumerous studies have shown that magnetic resonance imaging (MRI)-targeted biopsy approaches are superior to traditional systematic transrectal ultrasound guided biopsy (TRUS-Bx). The optimal number of biopsy cores to be obtained per lesion identified on multiparametric MRI (mpMRI) images, however, remains a matter of debate. The aim of this study was to evaluate the incremental value of additional biopsy cores in an MRI-targeted “in-bore”-biopsy (MRI-Bx) setting.Patients and methodsTwo hundred and forty-five patients, who underwent MRI-Bx between June 2014 and September 2021, were included in this retrospective single-center analysis. All lesions were biopsied with at least five biopsy cores and cumulative detection rates for any cancer (PCa) as well as detection rates of clinically significant cancers (csPCa) were calculated for each sequentially labeled biopsy core. The cumulative per-core detection rates are presented as whole numbers and as proportion of the maximum detection rate reached, when all biopsy cores were considered. CsPCa was defined as Gleason Score (GS) ≥ 7 (3 + 4).ResultsOne hundred and thirty-two of 245 Patients (53.9%) were diagnosed with prostate cancer and csPCa was found in 64 (26.1%) patients. The first biopsy core revealed csPCa/ PCa in 76.6% (49/64)/ 81.8% (108/132) of cases. The second, third and fourth core found csPCa/ PCa not detected by previous cores in 10.9% (7/64)/ 8.3% (11/132), 7.8% (5/64)/ 5.3% (7/132) and 3.1% (2/64)/ 3% (4/132) of cases, respectively. Obtaining one or more cores beyond the fourth biopsy core resulted in an increase in detection rate of 1.6% (1/64)/ 1.5% (2/132).ConclusionWe found that obtaining five cores per lesion maximized detection rates. If, however, future research should establish a clear link between the incidence of serious complications and the number of biopsy cores obtained, a three-core biopsy might suffice as our results suggest that about 95% of all csPCa are detected by the first three cores.

Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

ObjectiveSegmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning. MethodsThis study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s. ResultsCompared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches. ConclusionThe proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.

Optimizing anomaly detection in 3D MRI scans: The role of ConvLSTM in medical image analysis

The analysis of Medical Images (MI), particularly the detection and classification of anomalies in 3D MRI (Magnetic Resonance Imaging) scans, plays a critical part in timely intervention and personalized therapy plans. In our paper, a comprehensive methodology for anomaly detection in 3D MRI scans of the brain is proposed that combines advanced Deep Learning (DL) techniques, including Convolutional Long Short-Term Memory (ConvLSTM) model, with efficient statistical feature extraction from segmented anomaly regions. The data collection phase utilizes three main datasets namely the Brain Tumor Segmentation Challenge (BRATS), the Federated Tumor Segmentation Challenge (FETS), and the Medical Segmentation Decathlon (MSD). The research begins with preprocessing steps including image resizing, intensity normalization, and alignment of anomaly region segmentation masks. The targeted anomaly regions within the samples are segmented using U-Net architecture and then follow statistical feature extraction procedure. Dimensionality reduction methods such as Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are utilized to streamline the feature space. The ConvLSTM is then used to classify the anomalies using both convolution and recurrent layers to capture spatiotemporal patterns in MRI data. The model is fine-tuned and iterated for better classification performance using Adam optimizer. The statistical evaluation results with accuracy of 98.9 % showed that the designed ConvLSTM method is more suitable for clinical diagnosis and treatment design in detecting anomalies in 3D MRI images.

An Implementation of Effective CNN Model for AD Detection

This paper focuses on detecting Alzheimer’s Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

Reversible Cerebral Vasoconstriction Syndrome during Cesarean Section Complicated by Seizure Activity, Intraparenchymal and Subarachnoid Hemorrhage

Body of the Abstract: (1) To report a rare case of Reversible Cerebral Vasoconstriction Syndrome (RCVS) and associated intraparenchymal and subarachnoid hemorrhage following induction of spinal anesthesia in an uncomplicated, elective C-section (2) To discuss its potentially life threatening or well-being altering effects (3) To review the pathophysiology, diagnosis and potential treatments of the syndrome. Introduction: Reversible cerebral vasoconstriction syndrome (RCVS) is a rare condition defined clinically by symptoms of a severe recurrent headache described as a thunderclap headache, cerebral vasoconstriction of at least two different arteries on imaging, and resolution of vasoconstriction within three months. Complications include non-aneurysmal subarachnoid hemorrhage, seizure, stroke, and intracerebral hemorrhage. More than half of cases of RCVS occur postpartum or after use of vasoactive substances such as adrenergic or serotonergic drugs. Triggers include illicit drugs, eclampsia, or strenuous physical or sexual activity. As RCVS most commonly occurs during the postpartum period, it is extremely rare to encounter RCVS during the antepartum or peripartum periods. There has been only one other reported case of RCVS while undergoing spinal anesthesia for a C-section. Presented is a patient diagnosed with RCVS after spinal anesthesia during an elective C-section subsequently complicated by seizure, intracerebral and subarachnoid hemorrhage. Case Presentation: A 32-yearold G2 now P2002 patient presented at 39 6/7 weeks gestational age for elective primary C-section for a history of delivery complications with the first pregnancy. Medical history was unremarkable. First pregnancy labor epidural analgesia was uncomplicated. Spinal anesthesia induction was uneventful. Shortly thereafter, dizziness was reported and hypotension and bradycardia (P 42, BP 76/41) developed. Treatment included IV Ephedrine and Glycopyrrolate. Frontal headache developed. The patient then developed a tonic-clonic seizure with gaze deviation and upper limb rigidity lasting 45 seconds. An oral airway was placed for respiratory assistance with 100% oxygen. The patient became alert five minutes later. Vital signs: BP 168/98 P 122.C-section was performed. After incision, the patient had a second tonic-clonic seizure lasting 30 seconds. Midazolam was given. A healthy baby was delivered with Apgars of 7 and 9. The C-section was otherwise uncomplicated. Postoperatively, the patient was alert with stable vital signs. The neurologic exam was intact. The CT showed a 1.9 x 3.8 x 1.2 cm intraparenchymal hemorrhage-right frontal lobe with a subarachnoid hemorrhage in both the right frontal lobe and right sylvian fissure. Exam was normal. Headache continued. Tertiary care facility transfer was arranged. Tertiary care evaluation with 24-hour EEG monitoring, CT angiography (CTA) and CT venogram showed no evidence of venous sinus or cortical vein thrombosis. Magnetic resonance imaging (MRI) and transcranial doppler showed vasospasm diagnostic of RCVS. Subsequent CTA showed vasospasm of three branches of the carotid artery. Hospital discharge occurred on day 6. Six-month neurological follow-up showed resolution of hemorrhage and minimal residual symptoms. Discussion: Literature review discovered only one other case of RCVS with induction of regional anesthesia. To our knowledge the associated seizure activity coupled with intraparenchymal/subarachnoid hemorrhages has not been previously reported. RCVS, an extremely rare phenomenon is estimated to occur in 0.26% of headache patients; is more frequent in middle aged women and may have variable triggering factors. It may be associated with antidepressants, illicit drugs or sympathomimetic meds. Pregnancy and vasoconstrictive meds are risk factors with an occurrence rate of 5%. The pathophysiology is unknown but felt to be due to impaired cerebral vessel autoregulation. It is hypothesized that other factors and substances including endothelial dysfunction, pro and antiangiogenic factors, serotonin, cytokines and vascular endothelial growth factor may play some role. While MRI and doppler imaging may be of assistance, CTA is most useful for diagnosis. Since static imaging alone does not confirm the diagnosis, most cases likely go undiagnosed. Rarely, complications of RCVS include stroke, seizure, cerebral hemorrhage and Posterior Reversible Encephalopathy Syndrome. There is no prophylactic or therapeutic treatment proven effective for RCVS. Recommendations include symptomatic treatment of the headache, blood pressure, and delivery if eclampsia is suspected. Calcium channel blockers are often used for vasodilatory effects, although they have not been proven to improve symptoms or outcome in RCVS. Conclusion: RCVS is a very rarely occurring vascular disorder occasionally seen postpartum. This is the first reported case of associated seizure activity and resultant intraparenchymal and subarachnoid hemorrhage occurring after induction of spinal anesthesia for an uncomplicated C-section. It demonstrates the need for awareness of this disorder as well as knowledgeable hemodynamic anesthesia management specific to its unique features. The RCVS diagnostic and management challenges dictate that case collection and research be continued.